{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fully-Connected Neural Nets\n",
    "In the previous homework you implemented a fully-connected two-layer neural network on CIFAR-10. The implementation was simple but not very modular since the loss and gradient were computed in a single monolithic function. This is manageable for a simple two-layer network, but would become impractical as we move to bigger models. Ideally we want to build networks using a more modular design so that we can implement different layer types in isolation and then snap them together into models with different architectures.\n",
    "\n",
    "In this exercise we will implement fully-connected networks using a more modular approach. For each layer we will implement a `forward` and a `backward` function. The `forward` function will receive inputs, weights, and other parameters and will return both an output and a `cache` object storing data needed for the backward pass, like this:\n",
    "\n",
    "```python\n",
    "def layer_forward(x, w):\n",
    "  \"\"\" Receive inputs x and weights w \"\"\"\n",
    "  # Do some computations ...\n",
    "  z = # ... some intermediate value\n",
    "  # Do some more computations ...\n",
    "  out = # the output\n",
    "   \n",
    "  cache = (x, w, z, out) # Values we need to compute gradients\n",
    "   \n",
    "  return out, cache\n",
    "```\n",
    "\n",
    "The backward pass will receive upstream derivatives and the `cache` object, and will return gradients with respect to the inputs and weights, like this:\n",
    "\n",
    "```python\n",
    "def layer_backward(dout, cache):\n",
    "  \"\"\"\n",
    "  Receive dout (derivative of loss with respect to outputs) and cache,\n",
    "  and compute derivative with respect to inputs.\n",
    "  \"\"\"\n",
    "  # Unpack cache values\n",
    "  x, w, z, out = cache\n",
    "  \n",
    "  # Use values in cache to compute derivatives\n",
    "  dx = # Derivative of loss with respect to x\n",
    "  dw = # Derivative of loss with respect to w\n",
    "  \n",
    "  return dx, dw\n",
    "```\n",
    "\n",
    "After implementing a bunch of layers this way, we will be able to easily combine them to build classifiers with different architectures.\n",
    "\n",
    "In addition to implementing fully-connected networks of arbitrary depth, we will also explore different update rules for optimization, and introduce Dropout as a regularizer and Batch/Layer Normalization as a tool to more efficiently optimize deep networks.\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "run the following from the cs231n directory and try again:\n",
      "python setup.py build_ext --inplace\n",
      "You may also need to restart your iPython kernel\n"
     ]
    }
   ],
   "source": [
    "# As usual, a bit of setup\n",
    "from __future__ import print_function\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.fc_net import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "  \"\"\" returns relative error \"\"\"\n",
    "  return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('X_train: ', (49000, 3, 32, 32))\n",
      "('y_train: ', (49000,))\n",
      "('X_val: ', (1000, 3, 32, 32))\n",
      "('y_val: ', (1000,))\n",
      "('X_test: ', (1000, 3, 32, 32))\n",
      "('y_test: ', (1000,))\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in list(data.items()):\n",
    "  print(('%s: ' % k, v.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Affine layer: foward\n",
    "Open the file `cs231n/layers.py` and implement the `affine_forward` function.\n",
    "\n",
    "Once you are done you can test your implementaion by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 4, 5, 6)\n",
      "[[1.49834967 1.70660132 1.91485297]\n",
      " [3.25553199 3.5141327  3.77273342]]\n",
      "Testing affine_forward function:\n",
      "difference:  9.769847728806635e-10\n"
     ]
    }
   ],
   "source": [
    "# Test the affine_forward function\n",
    "\n",
    "num_inputs = 2\n",
    "input_shape = (4, 5, 6)#4*5*6\n",
    "output_dim = 3\n",
    "\n",
    "input_size = num_inputs * np.prod(input_shape)\n",
    "weight_size = output_dim * np.prod(input_shape)\n",
    "\n",
    "x = np.linspace(-0.1, 0.5, num=input_size).reshape(num_inputs, *input_shape)#元组，需要加上*\n",
    "w = np.linspace(-0.2, 0.3, num=weight_size).reshape(np.prod(input_shape), output_dim)\n",
    "b = np.linspace(-0.3, 0.1, num=output_dim)\n",
    "print(x.shape)\n",
    "out, _ = affine_forward(x, w, b)\n",
    "print(out)\n",
    "correct_out = np.array([[ 1.49834967,  1.70660132,  1.91485297],\n",
    "                        [ 3.25553199,  3.5141327,   3.77273342]])\n",
    "\n",
    "# Compare your output with ours. The error should be around e-9 or less.\n",
    "print('Testing affine_forward function:')\n",
    "print('difference: ', rel_error(out, correct_out))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Affine layer: backward\n",
    "Now implement the `affine_backward` function and test your implementation using numeric gradient checking."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_backward function:\n",
      "dx error:  5.399100368651805e-11\n",
      "dw error:  9.904211865398145e-11\n",
      "db error:  2.4122867568119087e-11\n"
     ]
    }
   ],
   "source": [
    "# Test the affine_backward function\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(10, 2, 3)\n",
    "w = np.random.randn(6, 5)\n",
    "b = np.random.randn(5)\n",
    "dout = np.random.randn(10, 5)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: affine_forward(x, w, b)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: affine_forward(x, w, b)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: affine_forward(x, w, b)[0], b, dout)\n",
    "\n",
    "_, cache = affine_forward(x, w, b)\n",
    "dx, dw, db = affine_backward(dout, cache)\n",
    "\n",
    "# The error should be around e-10 or less\n",
    "print('Testing affine_backward function:')\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dw error: ', rel_error(dw_num, dw))\n",
    "print('db error: ', rel_error(db_num, db))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ReLU activation: forward\n",
    "Implement the forward pass for the ReLU activation function in the `relu_forward` function and test your implementation using the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing relu_forward function:\n",
      "difference:  4.999999798022158e-08\n"
     ]
    }
   ],
   "source": [
    "# Test the relu_forward function\n",
    "\n",
    "x = np.linspace(-0.5, 0.5, num=12).reshape(3, 4)\n",
    "\n",
    "out, _ = relu_forward(x)\n",
    "correct_out = np.array([[ 0.,          0.,          0.,          0.,        ],\n",
    "                        [ 0.,          0.,          0.04545455,  0.13636364,],\n",
    "                        [ 0.22727273,  0.31818182,  0.40909091,  0.5,       ]])\n",
    "\n",
    "# Compare your output with ours. The error should be on the order of e-8\n",
    "print('Testing relu_forward function:')\n",
    "print('difference: ', rel_error(out, correct_out))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ReLU activation: backward\n",
    "Now implement the backward pass for the ReLU activation function in the `relu_backward` function and test your implementation using numeric gradient checking:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing relu_backward function:\n",
      "dx error:  3.2756349136310288e-12\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "x = np.random.randn(10, 10)\n",
    "dout = np.random.randn(*x.shape)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: relu_forward(x)[0], x, dout)\n",
    "\n",
    "_, cache = relu_forward(x)\n",
    "dx = relu_backward(dout, cache)\n",
    "\n",
    "# The error should be on the order of e-12\n",
    "print('Testing relu_backward function:')\n",
    "print('dx error: ', rel_error(dx_num, dx))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inline Question 1: \n",
    "\n",
    "We've only asked you to implement ReLU, but there are a number of different activation functions that one could use in neural networks, each with its pros and cons. In particular, an issue commonly seen with activation functions is getting zero (or close to zero) gradient flow during backpropagation. Which of the following activation functions have this problem? If you consider these functions in the one dimensional case, what types of input would lead to this behaviour?\n",
    "1. Sigmoid\n",
    "2. ReLU\n",
    "3. Leaky ReLU"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Answer:\n",
    "[Sigmoid]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# \"Sandwich\" layers\n",
    "There are some common patterns of layers that are frequently used in neural nets. For example, affine layers are frequently followed by a ReLU nonlinearity. To make these common patterns easy, we define several convenience layers in the file `cs231n/layer_utils.py`.\n",
    "\n",
    "For now take a look at the `affine_relu_forward` and `affine_relu_backward` functions, and run the following to numerically gradient check the backward pass:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_relu_forward and affine_relu_backward:\n",
      "dx error:  6.750562121603446e-11\n",
      "dw error:  8.162015570444288e-11\n",
      "db error:  7.826724021458994e-12\n"
     ]
    }
   ],
   "source": [
    "from cs231n.layer_utils import affine_relu_forward, affine_relu_backward\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(2, 3, 4)\n",
    "w = np.random.randn(12, 10)\n",
    "b = np.random.randn(10)\n",
    "dout = np.random.randn(2, 10)\n",
    "\n",
    "out, cache = affine_relu_forward(x, w, b)\n",
    "dx, dw, db = affine_relu_backward(dout, cache)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: affine_relu_forward(x, w, b)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: affine_relu_forward(x, w, b)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: affine_relu_forward(x, w, b)[0], b, dout)\n",
    "\n",
    "# Relative error should be around e-10 or less\n",
    "print('Testing affine_relu_forward and affine_relu_backward:')\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dw error: ', rel_error(dw_num, dw))\n",
    "print('db error: ', rel_error(db_num, db))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loss layers: Softmax and SVM\n",
    "You implemented these loss functions in the last assignment, so we'll give them to you for free here. You should still make sure you understand how they work by looking at the implementations in `cs231n/layers.py`.\n",
    "\n",
    "You can make sure that the implementations are correct by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing svm_loss:\n",
      "loss:  8.999602749096233\n",
      "dx error:  1.4021566006651672e-09\n",
      "\n",
      "Testing softmax_loss:\n",
      "loss:  2.302545844500738\n",
      "dx error:  9.384673161989355e-09\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "num_classes, num_inputs = 10, 50\n",
    "x = 0.001 * np.random.randn(num_inputs, num_classes)\n",
    "y = np.random.randint(num_classes, size=num_inputs)\n",
    "\n",
    "dx_num = eval_numerical_gradient(lambda x: svm_loss(x, y)[0], x, verbose=False)\n",
    "loss, dx = svm_loss(x, y)\n",
    "\n",
    "# Test svm_loss function. Loss should be around 9 and dx error should be around the order of e-9\n",
    "print('Testing svm_loss:')\n",
    "print('loss: ', loss)\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "\n",
    "dx_num = eval_numerical_gradient(lambda x: softmax_loss(x, y)[0], x, verbose=False)\n",
    "loss, dx = softmax_loss(x, y)\n",
    "\n",
    "# Test softmax_loss function. Loss should be close to 2.3 and dx error should be around e-8\n",
    "print('\\nTesting softmax_loss:')\n",
    "print('loss: ', loss)\n",
    "print('dx error: ', rel_error(dx_num, dx))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Two-layer network\n",
    "In the previous assignment you implemented a two-layer neural network in a single monolithic class. Now that you have implemented modular versions of the necessary layers, you will reimplement the two layer network using these modular implementations.\n",
    "\n",
    "Open the file `cs231n/classifiers/fc_net.py` and complete the implementation of the `TwoLayerNet` class. This class will serve as a model for the other networks you will implement in this assignment, so read through it to make sure you understand the API. You can run the cell below to test your implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.83015473 -0.4102416  -0.60504617]\n",
      " [ 1.1170258   0.55009541  1.56403459]]\n"
     ]
    }
   ],
   "source": [
    "print(np.random.normal(0,1,(2,3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing initialization ... \n",
      "Testing test-time forward pass ... \n",
      "Testing training loss (no regularization)\n",
      "Running numeric gradient check with reg =  0.0\n",
      "W1 relative error: 1.52e-08\n",
      "W2 relative error: 3.48e-10\n",
      "b1 relative error: 6.55e-09\n",
      "b2 relative error: 4.33e-10\n",
      "Running numeric gradient check with reg =  0.7\n",
      "W1 relative error: 8.18e-07\n",
      "W2 relative error: 2.85e-08\n",
      "b1 relative error: 1.09e-09\n",
      "b2 relative error: 7.76e-10\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H, C = 3, 5, 50, 7\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=N)\n",
    "\n",
    "std = 1e-3\n",
    "model = TwoLayerNet(input_dim=D, hidden_dim=H, num_classes=C, weight_scale=std)\n",
    "\n",
    "print('Testing initialization ... ')\n",
    "W1_std = abs(model.params['W1'].std() - std)\n",
    "b1 = model.params['b1']\n",
    "W2_std = abs(model.params['W2'].std() - std)\n",
    "b2 = model.params['b2']\n",
    "assert W1_std < std / 10, 'First layer weights do not seem right'#assert声明为真\n",
    "assert np.all(b1 == 0), 'First layer biases do not seem right'\n",
    "assert W2_std < std / 10, 'Second layer weights do not seem right'\n",
    "assert np.all(b2 == 0), 'Second layer biases do not seem right'\n",
    "\n",
    "print('Testing test-time forward pass ... ')\n",
    "model.params['W1'] = np.linspace(-0.7, 0.3, num=D*H).reshape(D, H)\n",
    "model.params['b1'] = np.linspace(-0.1, 0.9, num=H)\n",
    "model.params['W2'] = np.linspace(-0.3, 0.4, num=H*C).reshape(H, C)\n",
    "model.params['b2'] = np.linspace(-0.9, 0.1, num=C)\n",
    "X = np.linspace(-5.5, 4.5, num=N*D).reshape(D, N).T\n",
    "scores = model.loss(X)\n",
    "correct_scores = np.asarray(\n",
    "  [[11.53165108,  12.2917344,   13.05181771,  13.81190102,  14.57198434, 15.33206765,  16.09215096],\n",
    "   [12.05769098,  12.74614105,  13.43459113,  14.1230412,   14.81149128, 15.49994135,  16.18839143],\n",
    "   [12.58373087,  13.20054771,  13.81736455,  14.43418138,  15.05099822, 15.66781506,  16.2846319 ]])\n",
    "scores_diff = np.abs(scores - correct_scores).sum()\n",
    "assert scores_diff < 1e-6, 'Problem with test-time forward pass'\n",
    "\n",
    "print('Testing training loss (no regularization)')\n",
    "y = np.asarray([0, 5, 1])\n",
    "loss, grads = model.loss(X, y)\n",
    "correct_loss = 3.4702243556\n",
    "assert abs(loss - correct_loss) < 1e-10, 'Problem with training-time loss'\n",
    "\n",
    "model.reg = 1.0\n",
    "loss, grads = model.loss(X, y)\n",
    "correct_loss = 26.5948426952\n",
    "assert abs(loss - correct_loss) < 1e-10, 'Problem with regularization loss'\n",
    "\n",
    "# Errors should be around e-7 or less\n",
    "for reg in [0.0, 0.7]:\n",
    "  print('Running numeric gradient check with reg = ', reg)\n",
    "  model.reg = reg\n",
    "  loss, grads = model.loss(X, y)\n",
    "\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False)\n",
    "    print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Solver\n",
    "In the previous assignment, the logic for training models was coupled to the models themselves. Following a more modular design, for this assignment we have split the logic for training models into a separate class.\n",
    "\n",
    "Open the file `cs231n/solver.py` and read through it to familiarize yourself with the API. After doing so, use a `Solver` instance to train a `TwoLayerNet` that achieves at least `50%` accuracy on the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 4900) loss: 2.305775\n",
      "(Epoch 0 / 10) train acc: 0.137000; val_acc: 0.134000\n",
      "(Iteration 101 / 4900) loss: 1.848770\n",
      "(Iteration 201 / 4900) loss: 1.847185\n",
      "(Iteration 301 / 4900) loss: 1.662817\n",
      "(Iteration 401 / 4900) loss: 1.606323\n",
      "(Epoch 1 / 10) train acc: 0.438000; val_acc: 0.444000\n",
      "(Iteration 501 / 4900) loss: 1.603248\n",
      "(Iteration 601 / 4900) loss: 1.641613\n",
      "(Iteration 701 / 4900) loss: 1.566951\n",
      "(Iteration 801 / 4900) loss: 1.448262\n",
      "(Iteration 901 / 4900) loss: 1.448082\n",
      "(Epoch 2 / 10) train acc: 0.490000; val_acc: 0.434000\n",
      "(Iteration 1001 / 4900) loss: 1.379479\n",
      "(Iteration 1101 / 4900) loss: 1.518438\n",
      "(Iteration 1201 / 4900) loss: 1.450331\n",
      "(Iteration 1301 / 4900) loss: 1.419421\n",
      "(Iteration 1401 / 4900) loss: 1.493814\n",
      "(Epoch 3 / 10) train acc: 0.522000; val_acc: 0.473000\n",
      "(Iteration 1501 / 4900) loss: 1.417775\n",
      "(Iteration 1601 / 4900) loss: 1.624403\n",
      "(Iteration 1701 / 4900) loss: 1.268404\n",
      "(Iteration 1801 / 4900) loss: 1.489844\n",
      "(Iteration 1901 / 4900) loss: 1.279799\n",
      "(Epoch 4 / 10) train acc: 0.566000; val_acc: 0.491000\n",
      "(Iteration 2001 / 4900) loss: 1.208832\n",
      "(Iteration 2101 / 4900) loss: 1.454004\n",
      "(Iteration 2201 / 4900) loss: 1.175380\n",
      "(Iteration 2301 / 4900) loss: 1.107698\n",
      "(Iteration 2401 / 4900) loss: 1.182936\n",
      "(Epoch 5 / 10) train acc: 0.582000; val_acc: 0.515000\n",
      "(Iteration 2501 / 4900) loss: 1.212957\n",
      "(Iteration 2601 / 4900) loss: 1.576515\n",
      "(Iteration 2701 / 4900) loss: 1.241845\n",
      "(Iteration 2801 / 4900) loss: 1.124701\n",
      "(Iteration 2901 / 4900) loss: 1.252549\n",
      "(Epoch 6 / 10) train acc: 0.599000; val_acc: 0.514000\n",
      "(Iteration 3001 / 4900) loss: 1.140826\n",
      "(Iteration 3101 / 4900) loss: 1.279448\n",
      "(Iteration 3201 / 4900) loss: 1.150392\n",
      "(Iteration 3301 / 4900) loss: 1.284721\n",
      "(Iteration 3401 / 4900) loss: 1.131664\n",
      "(Epoch 7 / 10) train acc: 0.553000; val_acc: 0.484000\n",
      "(Iteration 3501 / 4900) loss: 1.257030\n",
      "(Iteration 3601 / 4900) loss: 1.258260\n",
      "(Iteration 3701 / 4900) loss: 1.145470\n",
      "(Iteration 3801 / 4900) loss: 1.163656\n",
      "(Iteration 3901 / 4900) loss: 1.226868\n",
      "(Epoch 8 / 10) train acc: 0.615000; val_acc: 0.528000\n",
      "(Iteration 4001 / 4900) loss: 1.045477\n",
      "(Iteration 4101 / 4900) loss: 1.472310\n",
      "(Iteration 4201 / 4900) loss: 1.180047\n",
      "(Iteration 4301 / 4900) loss: 1.098359\n",
      "(Iteration 4401 / 4900) loss: 1.067490\n",
      "(Epoch 9 / 10) train acc: 0.619000; val_acc: 0.508000\n",
      "(Iteration 4501 / 4900) loss: 1.394357\n",
      "(Iteration 4601 / 4900) loss: 1.026986\n",
      "(Iteration 4701 / 4900) loss: 1.130316\n",
      "(Iteration 4801 / 4900) loss: 1.227762\n",
      "(Epoch 10 / 10) train acc: 0.621000; val_acc: 0.498000\n"
     ]
    }
   ],
   "source": [
    "model = TwoLayerNet()\n",
    "solver = None\n",
    "\n",
    "##############################################################################\n",
    "# TODO: Use a Solver instance to train a TwoLayerNet that achieves at least  #\n",
    "# 50% accuracy on the validation set.                                        #\n",
    "##############################################################################\n",
    "solver = Solver(model, data,\n",
    "                    update_rule='sgd',\n",
    "                    optim_config={\n",
    "                      'learning_rate': 1e-3,\n",
    "                    },\n",
    "                    lr_decay=0.95,\n",
    "                    num_epochs=10, batch_size=100,\n",
    "                    print_every=100)\n",
    "solver.train()\n",
    "##############################################################################\n",
    "#                             END OF YOUR CODE                               #\n",
    "##############################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run this cell to visualize training loss and train / val accuracy\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.title('Accuracy')\n",
    "plt.plot(solver.train_acc_history, '-o', label='train')\n",
    "plt.plot(solver.val_acc_history, '-o', label='val')\n",
    "plt.plot([0.5] * len(solver.val_acc_history), 'k--')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(loc='lower right')\n",
    "plt.gcf().set_size_inches(15, 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multilayer network\n",
    "Next you will implement a fully-connected network with an arbitrary number of hidden layers.\n",
    "\n",
    "Read through the `FullyConnectedNet` class in the file `cs231n/classifiers/fc_net.py`.\n",
    "\n",
    "Implement the initialization, the forward pass, and the backward pass. For the moment don't worry about implementing dropout or batch/layer normalization; we will add those features soon."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial loss and gradient check"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a sanity check, run the following to check the initial loss and to gradient check the network both with and without regularization. Do the initial losses seem reasonable?\n",
    "\n",
    "For gradient checking, you should expect to see errors around 1e-7 or less."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running check with reg =  0\n",
      "Initial loss:  2.3004790897684924\n",
      "W1 relative error: 1.48e-07\n",
      "W2 relative error: 2.21e-05\n",
      "W3 relative error: 3.53e-07\n",
      "b1 relative error: 5.38e-09\n",
      "b2 relative error: 2.09e-09\n",
      "b3 relative error: 5.80e-11\n",
      "Running check with reg =  3.14\n",
      "Initial loss:  7.052114776533016\n",
      "W1 relative error: 7.36e-09\n",
      "W2 relative error: 6.87e-08\n",
      "W3 relative error: 3.48e-08\n",
      "b1 relative error: 1.48e-08\n",
      "b2 relative error: 1.72e-09\n",
      "b3 relative error: 1.80e-10\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=(N,))\n",
    "for reg in [0, 3.14]:\n",
    "    print('Running check with reg = ', reg)\n",
    "    model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n",
    "                            reg=reg, weight_scale=5e-2, dtype=np.float64)\n",
    "    loss, grads = model.loss(X, y)                          \n",
    "    print('Initial loss: ', loss)\n",
    "  \n",
    "    # Most of the errors should be on the order of e-7 or smaller.   \n",
    "    # NOTE: It is fine however to see an error for W2 on the order of e-5\n",
    "    # for the check when reg = 0.0\n",
    "    for name in sorted(grads):\n",
    "        f = lambda _: model.loss(X, y)[0]\n",
    "        grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n",
    "        print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As another sanity check, make sure you can overfit a small dataset of 50 images. First we will try a three-layer network with 100 units in each hidden layer. In the following cell, tweak the learning rate and initialization scale to overfit and achieve 100% training accuracy within 20 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 40) loss: 2.307416\n",
      "(Epoch 0 / 20) train acc: 0.320000; val_acc: 0.145000\n",
      "(Epoch 1 / 20) train acc: 0.260000; val_acc: 0.140000\n",
      "(Epoch 2 / 20) train acc: 0.360000; val_acc: 0.144000\n",
      "(Epoch 3 / 20) train acc: 0.320000; val_acc: 0.099000\n",
      "(Epoch 4 / 20) train acc: 0.680000; val_acc: 0.169000\n",
      "(Epoch 5 / 20) train acc: 0.580000; val_acc: 0.161000\n",
      "(Iteration 11 / 40) loss: 1.385911\n",
      "(Epoch 6 / 20) train acc: 0.660000; val_acc: 0.173000\n",
      "(Epoch 7 / 20) train acc: 0.680000; val_acc: 0.195000\n",
      "(Epoch 8 / 20) train acc: 0.780000; val_acc: 0.195000\n",
      "(Epoch 9 / 20) train acc: 0.820000; val_acc: 0.184000\n",
      "(Epoch 10 / 20) train acc: 0.840000; val_acc: 0.178000\n",
      "(Iteration 21 / 40) loss: 0.540126\n",
      "(Epoch 11 / 20) train acc: 0.900000; val_acc: 0.174000\n",
      "(Epoch 12 / 20) train acc: 0.900000; val_acc: 0.178000\n",
      "(Epoch 13 / 20) train acc: 0.920000; val_acc: 0.193000\n",
      "(Epoch 14 / 20) train acc: 0.960000; val_acc: 0.193000\n",
      "(Epoch 15 / 20) train acc: 0.960000; val_acc: 0.195000\n",
      "(Iteration 31 / 40) loss: 0.090138\n",
      "(Epoch 16 / 20) train acc: 0.980000; val_acc: 0.187000\n",
      "(Epoch 17 / 20) train acc: 0.980000; val_acc: 0.194000\n",
      "(Epoch 18 / 20) train acc: 0.980000; val_acc: 0.184000\n",
      "(Epoch 19 / 20) train acc: 1.000000; val_acc: 0.186000\n",
      "(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.200000\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Use a three-layer Net to overfit 50 training examples by \n",
    "# tweaking just the learning rate and initialization scale.\n",
    "\n",
    "num_train = 50\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "weight_scale = 1e-2\n",
    "learning_rate = 7e-3\n",
    "model = FullyConnectedNet([100, 100],\n",
    "              weight_scale=weight_scale, dtype=np.float64)\n",
    "solver = Solver(model, small_data,\n",
    "                print_every=10, num_epochs=20, batch_size=25,\n",
    "                update_rule='sgd',\n",
    "                optim_config={\n",
    "                  'learning_rate': learning_rate,\n",
    "                }\n",
    "         )\n",
    "solver.train()\n",
    "\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.title('Training loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Training loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now try to use a five-layer network with 100 units on each layer to overfit 50 training examples. Again you will have to adjust the learning rate and weight initialization, but you should be able to achieve 100% training accuracy within 20 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 40) loss: 5.298418\n",
      "(Epoch 0 / 20) train acc: 0.220000; val_acc: 0.085000\n",
      "(Epoch 1 / 20) train acc: 0.320000; val_acc: 0.094000\n",
      "(Epoch 2 / 20) train acc: 0.480000; val_acc: 0.091000\n",
      "(Epoch 3 / 20) train acc: 0.580000; val_acc: 0.107000\n",
      "(Epoch 4 / 20) train acc: 0.680000; val_acc: 0.111000\n",
      "(Epoch 5 / 20) train acc: 0.740000; val_acc: 0.104000\n",
      "(Iteration 11 / 40) loss: 0.785088\n",
      "(Epoch 6 / 20) train acc: 0.820000; val_acc: 0.127000\n",
      "(Epoch 7 / 20) train acc: 0.900000; val_acc: 0.129000\n",
      "(Epoch 8 / 20) train acc: 0.940000; val_acc: 0.123000\n",
      "(Epoch 9 / 20) train acc: 0.960000; val_acc: 0.125000\n",
      "(Epoch 10 / 20) train acc: 0.980000; val_acc: 0.120000\n",
      "(Iteration 21 / 40) loss: 0.348084\n",
      "(Epoch 11 / 20) train acc: 0.980000; val_acc: 0.136000\n",
      "(Epoch 12 / 20) train acc: 0.980000; val_acc: 0.123000\n",
      "(Epoch 13 / 20) train acc: 0.960000; val_acc: 0.127000\n",
      "(Epoch 14 / 20) train acc: 0.980000; val_acc: 0.135000\n",
      "(Epoch 15 / 20) train acc: 1.000000; val_acc: 0.132000\n",
      "(Iteration 31 / 40) loss: 0.261627\n",
      "(Epoch 16 / 20) train acc: 1.000000; val_acc: 0.127000\n",
      "(Epoch 17 / 20) train acc: 1.000000; val_acc: 0.129000\n",
      "(Epoch 18 / 20) train acc: 1.000000; val_acc: 0.128000\n",
      "(Epoch 19 / 20) train acc: 1.000000; val_acc: 0.131000\n",
      "(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.135000\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Use a five-layer Net to overfit 50 training examples by \n",
    "# tweaking just the learning rate and initialization scale.\n",
    "\n",
    "num_train = 50\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "learning_rate = 2e-3\n",
    "weight_scale = 5e-2\n",
    "model = FullyConnectedNet([100, 100, 100, 100],\n",
    "                weight_scale=weight_scale, dtype=np.float64)\n",
    "solver = Solver(model, small_data,\n",
    "                print_every=10, num_epochs=20, batch_size=25,\n",
    "                update_rule='sgd',\n",
    "                optim_config={\n",
    "                  'learning_rate': learning_rate,\n",
    "                }\n",
    "         )\n",
    "solver.train()\n",
    "\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.title('Training loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Training loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inline Question 2: \n",
    "Did you notice anything about the comparative difficulty of training the three-layer net vs training the five layer net? In particular, based on your experience, which network seemed more sensitive to the initialization scale? Why do you think that is the case?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Answer:\n",
    "[FILL THIS IN]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Update rules\n",
    "So far we have used vanilla stochastic gradient descent (SGD) as our update rule. More sophisticated update rules can make it easier to train deep networks. We will implement a few of the most commonly used update rules and compare them to vanilla SGD."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SGD+Momentum\n",
    "Stochastic gradient descent with momentum is a widely used update rule that tends to make deep networks converge faster than vanilla stochastic gradient descent. See the Momentum Update section at http://cs231n.github.io/neural-networks-3/#sgd for more information.\n",
    "\n",
    "Open the file `cs231n/optim.py` and read the documentation at the top of the file to make sure you understand the API. Implement the SGD+momentum update rule in the function `sgd_momentum` and run the following to check your implementation. You should see errors less than e-8."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  8.882347033505819e-09\n",
      "velocity error:  4.269287743278663e-09\n"
     ]
    }
   ],
   "source": [
    "from cs231n.optim import sgd_momentum\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "v = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-3, 'velocity': v}\n",
    "next_w, _ = sgd_momentum(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [ 0.1406,      0.20738947,  0.27417895,  0.34096842,  0.40775789],\n",
    "  [ 0.47454737,  0.54133684,  0.60812632,  0.67491579,  0.74170526],\n",
    "  [ 0.80849474,  0.87528421,  0.94207368,  1.00886316,  1.07565263],\n",
    "  [ 1.14244211,  1.20923158,  1.27602105,  1.34281053,  1.4096    ]])\n",
    "expected_velocity = np.asarray([\n",
    "  [ 0.5406,      0.55475789,  0.56891579, 0.58307368,  0.59723158],\n",
    "  [ 0.61138947,  0.62554737,  0.63970526,  0.65386316,  0.66802105],\n",
    "  [ 0.68217895,  0.69633684,  0.71049474,  0.72465263,  0.73881053],\n",
    "  [ 0.75296842,  0.76712632,  0.78128421,  0.79544211,  0.8096    ]])\n",
    "\n",
    "# Should see relative errors around e-8 or less\n",
    "print('next_w error: ', rel_error(next_w, expected_next_w))\n",
    "print('velocity error: ', rel_error(expected_velocity, config['velocity']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you have done so, run the following to train a six-layer network with both SGD and SGD+momentum. You should see the SGD+momentum update rule converge faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running with  sgd\n",
      "(Iteration 1 / 200) loss: 2.683541\n",
      "(Epoch 0 / 5) train acc: 0.101000; val_acc: 0.094000\n",
      "(Iteration 11 / 200) loss: 2.225419\n",
      "(Iteration 21 / 200) loss: 2.129275\n",
      "(Iteration 31 / 200) loss: 2.038702\n",
      "(Epoch 1 / 5) train acc: 0.282000; val_acc: 0.226000\n",
      "(Iteration 41 / 200) loss: 1.923880\n",
      "(Iteration 51 / 200) loss: 1.941505\n",
      "(Iteration 61 / 200) loss: 1.879468\n",
      "(Iteration 71 / 200) loss: 2.059843\n",
      "(Epoch 2 / 5) train acc: 0.322000; val_acc: 0.276000\n",
      "(Iteration 81 / 200) loss: 1.891704\n",
      "(Iteration 91 / 200) loss: 1.756311\n",
      "(Iteration 101 / 200) loss: 1.720548\n",
      "(Iteration 111 / 200) loss: 1.707971\n",
      "(Epoch 3 / 5) train acc: 0.411000; val_acc: 0.282000\n",
      "(Iteration 121 / 200) loss: 1.732298\n",
      "(Iteration 131 / 200) loss: 1.600037\n",
      "(Iteration 141 / 200) loss: 1.635264\n",
      "(Iteration 151 / 200) loss: 1.748399\n",
      "(Epoch 4 / 5) train acc: 0.413000; val_acc: 0.305000\n",
      "(Iteration 161 / 200) loss: 1.664544\n",
      "(Iteration 171 / 200) loss: 1.832853\n",
      "(Iteration 181 / 200) loss: 1.781916\n",
      "(Iteration 191 / 200) loss: 1.772547\n",
      "(Epoch 5 / 5) train acc: 0.463000; val_acc: 0.313000\n",
      "\n",
      "running with  sgd_momentum\n",
      "(Iteration 1 / 200) loss: 2.483261\n",
      "(Epoch 0 / 5) train acc: 0.112000; val_acc: 0.116000\n",
      "(Iteration 11 / 200) loss: 2.126077\n",
      "(Iteration 21 / 200) loss: 2.040233\n",
      "(Iteration 31 / 200) loss: 1.859148\n",
      "(Epoch 1 / 5) train acc: 0.302000; val_acc: 0.276000\n",
      "(Iteration 41 / 200) loss: 1.906502\n",
      "(Iteration 51 / 200) loss: 1.811656\n",
      "(Iteration 61 / 200) loss: 1.652663\n",
      "(Iteration 71 / 200) loss: 1.835253\n",
      "(Epoch 2 / 5) train acc: 0.422000; val_acc: 0.310000\n",
      "(Iteration 81 / 200) loss: 1.644664\n",
      "(Iteration 91 / 200) loss: 1.661185\n",
      "(Iteration 101 / 200) loss: 1.589806\n",
      "(Iteration 111 / 200) loss: 1.583947\n",
      "(Epoch 3 / 5) train acc: 0.466000; val_acc: 0.342000\n",
      "(Iteration 121 / 200) loss: 1.622718\n",
      "(Iteration 131 / 200) loss: 1.566011\n",
      "(Iteration 141 / 200) loss: 1.574171\n",
      "(Iteration 151 / 200) loss: 1.418724\n",
      "(Epoch 4 / 5) train acc: 0.511000; val_acc: 0.356000\n",
      "(Iteration 161 / 200) loss: 1.567830\n",
      "(Iteration 171 / 200) loss: 1.477082\n",
      "(Iteration 181 / 200) loss: 1.547347\n",
      "(Iteration 191 / 200) loss: 1.551334\n",
      "(Epoch 5 / 5) train acc: 0.476000; val_acc: 0.333000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\Anaconda_install\\envs\\cs231n\\lib\\site-packages\\matplotlib\\cbook\\deprecation.py:107: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  warnings.warn(message, mplDeprecation, stacklevel=1)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_train = 4000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "solvers = {}\n",
    "\n",
    "for update_rule in ['sgd', 'sgd_momentum']:\n",
    "    print('running with ', update_rule)\n",
    "    model = FullyConnectedNet([100, 100, 100, 100, 100], weight_scale=5e-2)\n",
    "\n",
    "    solver = Solver(model, small_data,\n",
    "                  num_epochs=5, batch_size=100,\n",
    "                  update_rule=update_rule,\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-2,\n",
    "                  },\n",
    "                  verbose=True)\n",
    "    solvers[update_rule] = solver\n",
    "    solver.train()\n",
    "    print()\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "for update_rule, solver in list(solvers.items()):\n",
    "    plt.subplot(3, 1, 1)\n",
    "    plt.plot(solver.loss_history, 'o', label=update_rule)\n",
    "  \n",
    "    plt.subplot(3, 1, 2)\n",
    "    plt.plot(solver.train_acc_history, '-o', label=update_rule)\n",
    "\n",
    "    plt.subplot(3, 1, 3)\n",
    "    plt.plot(solver.val_acc_history, '-o', label=update_rule)\n",
    "    \n",
    "for i in [1, 2, 3]:\n",
    "    plt.subplot(3, 1, i)\n",
    "    plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RMSProp and Adam\n",
    "RMSProp [1] and Adam [2] are update rules that set per-parameter learning rates by using a running average of the second moments of gradients.\n",
    "\n",
    "In the file `cs231n/optim.py`, implement the RMSProp update rule in the `rmsprop` function and implement the Adam update rule in the `adam` function, and check your implementations using the tests below.\n",
    "\n",
    "**NOTE:** Please implement the _complete_ Adam update rule (with the bias correction mechanism), not the first simplified version mentioned in the course notes. \n",
    "\n",
    "[1] Tijmen Tieleman and Geoffrey Hinton. \"Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude.\" COURSERA: Neural Networks for Machine Learning 4 (2012).\n",
    "\n",
    "[2] Diederik Kingma and Jimmy Ba, \"Adam: A Method for Stochastic Optimization\", ICLR 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  9.524687511038133e-08\n",
      "cache error:  2.6477955807156126e-09\n"
     ]
    }
   ],
   "source": [
    "# Test RMSProp implementation\n",
    "from cs231n.optim import rmsprop\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "cache = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-2, 'cache': cache}\n",
    "next_w, _ = rmsprop(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [-0.39223849, -0.34037513, -0.28849239, -0.23659121, -0.18467247],\n",
    "  [-0.132737,   -0.08078555, -0.02881884,  0.02316247,  0.07515774],\n",
    "  [ 0.12716641,  0.17918792,  0.23122175,  0.28326742,  0.33532447],\n",
    "  [ 0.38739248,  0.43947102,  0.49155973,  0.54365823,  0.59576619]])\n",
    "expected_cache = np.asarray([\n",
    "  [ 0.5976,      0.6126277,   0.6277108,   0.64284931,  0.65804321],\n",
    "  [ 0.67329252,  0.68859723,  0.70395734,  0.71937285,  0.73484377],\n",
    "  [ 0.75037008,  0.7659518,   0.78158892,  0.79728144,  0.81302936],\n",
    "  [ 0.82883269,  0.84469141,  0.86060554,  0.87657507,  0.8926    ]])\n",
    "\n",
    "# You should see relative errors around e-7 or less\n",
    "print('next_w error: ', rel_error(expected_next_w, next_w))\n",
    "print('cache error: ', rel_error(expected_cache, config['cache']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  0.0015218451757856217\n",
      "v error:  4.208314038113071e-09\n",
      "m error:  4.214963193114416e-09\n"
     ]
    }
   ],
   "source": [
    "# Test Adam implementation\n",
    "from cs231n.optim import adam\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "m = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "v = np.linspace(0.7, 0.5, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-2, 'm': m, 'v': v, 't': 5}\n",
    "next_w, _ = adam(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [-0.40094747, -0.34836187, -0.29577703, -0.24319299, -0.19060977],\n",
    "  [-0.1380274,  -0.08544591, -0.03286534,  0.01971428,  0.0722929],\n",
    "  [ 0.1248705,   0.17744702,  0.23002243,  0.28259667,  0.33516969],\n",
    "  [ 0.38774145,  0.44031188,  0.49288093,  0.54544852,  0.59801459]])\n",
    "expected_v = np.asarray([\n",
    "  [ 0.69966,     0.68908382,  0.67851319,  0.66794809,  0.65738853,],\n",
    "  [ 0.64683452,  0.63628604,  0.6257431,   0.61520571,  0.60467385,],\n",
    "  [ 0.59414753,  0.58362676,  0.57311152,  0.56260183,  0.55209767,],\n",
    "  [ 0.54159906,  0.53110598,  0.52061845,  0.51013645,  0.49966,   ]])\n",
    "expected_m = np.asarray([\n",
    "  [ 0.48,        0.49947368,  0.51894737,  0.53842105,  0.55789474],\n",
    "  [ 0.57736842,  0.59684211,  0.61631579,  0.63578947,  0.65526316],\n",
    "  [ 0.67473684,  0.69421053,  0.71368421,  0.73315789,  0.75263158],\n",
    "  [ 0.77210526,  0.79157895,  0.81105263,  0.83052632,  0.85      ]])\n",
    "\n",
    "# You should see relative errors around e-7 or less\n",
    "print('next_w error: ', rel_error(expected_next_w, next_w))\n",
    "print('v error: ', rel_error(expected_v, config['v']))\n",
    "print('m error: ', rel_error(expected_m, config['m']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you have debugged your RMSProp and Adam implementations, run the following to train a pair of deep networks using these new update rules:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running with  adam\n",
      "(Iteration 1 / 200) loss: 2.557293\n",
      "(Epoch 0 / 5) train acc: 0.099000; val_acc: 0.097000\n",
      "(Iteration 11 / 200) loss: 2.088450\n",
      "(Iteration 21 / 200) loss: 2.172245\n",
      "(Iteration 31 / 200) loss: 1.807095\n",
      "(Epoch 1 / 5) train acc: 0.365000; val_acc: 0.330000\n",
      "(Iteration 41 / 200) loss: 1.766005\n",
      "(Iteration 51 / 200) loss: 1.622462\n",
      "(Iteration 61 / 200) loss: 1.706356\n",
      "(Iteration 71 / 200) loss: 1.681513\n",
      "(Epoch 2 / 5) train acc: 0.435000; val_acc: 0.371000\n",
      "(Iteration 81 / 200) loss: 1.542187\n",
      "(Iteration 91 / 200) loss: 1.469722\n",
      "(Iteration 101 / 200) loss: 1.474647\n",
      "(Iteration 111 / 200) loss: 1.413354\n",
      "(Epoch 3 / 5) train acc: 0.520000; val_acc: 0.389000\n",
      "(Iteration 121 / 200) loss: 1.756952\n",
      "(Iteration 131 / 200) loss: 1.493709\n",
      "(Iteration 141 / 200) loss: 1.316570\n",
      "(Iteration 151 / 200) loss: 1.290329\n",
      "(Epoch 4 / 5) train acc: 0.552000; val_acc: 0.371000\n",
      "(Iteration 161 / 200) loss: 0.992681\n",
      "(Iteration 171 / 200) loss: 1.323912\n",
      "(Iteration 181 / 200) loss: 1.222605\n",
      "(Iteration 191 / 200) loss: 1.291151\n",
      "(Epoch 5 / 5) train acc: 0.607000; val_acc: 0.389000\n",
      "\n",
      "running with  rmsprop\n",
      "(Iteration 1 / 200) loss: 2.471365\n",
      "(Epoch 0 / 5) train acc: 0.186000; val_acc: 0.162000\n",
      "(Iteration 11 / 200) loss: 2.009850\n",
      "(Iteration 21 / 200) loss: 1.852838\n",
      "(Iteration 31 / 200) loss: 1.879077\n",
      "(Epoch 1 / 5) train acc: 0.340000; val_acc: 0.301000\n",
      "(Iteration 41 / 200) loss: 1.752219\n",
      "(Iteration 51 / 200) loss: 1.698903\n",
      "(Iteration 61 / 200) loss: 1.714568\n",
      "(Iteration 71 / 200) loss: 1.608660\n",
      "(Epoch 2 / 5) train acc: 0.446000; val_acc: 0.355000\n",
      "(Iteration 81 / 200) loss: 1.831594\n",
      "(Iteration 91 / 200) loss: 1.606019\n",
      "(Iteration 101 / 200) loss: 1.783182\n",
      "(Iteration 111 / 200) loss: 1.641186\n",
      "(Epoch 3 / 5) train acc: 0.465000; val_acc: 0.351000\n",
      "(Iteration 121 / 200) loss: 1.717582\n",
      "(Iteration 131 / 200) loss: 1.580007\n",
      "(Iteration 141 / 200) loss: 1.567360\n",
      "(Iteration 151 / 200) loss: 1.420399\n",
      "(Epoch 4 / 5) train acc: 0.513000; val_acc: 0.346000\n",
      "(Iteration 161 / 200) loss: 1.652722\n",
      "(Iteration 171 / 200) loss: 1.295133\n",
      "(Iteration 181 / 200) loss: 1.331340\n",
      "(Iteration 191 / 200) loss: 1.345946\n",
      "(Epoch 5 / 5) train acc: 0.532000; val_acc: 0.340000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\Anaconda_install\\envs\\cs231n\\lib\\site-packages\\matplotlib\\cbook\\deprecation.py:107: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  warnings.warn(message, mplDeprecation, stacklevel=1)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learning_rates = {'rmsprop': 1e-4, 'adam': 1e-3}\n",
    "for update_rule in ['adam', 'rmsprop']:\n",
    "    print('running with ', update_rule)\n",
    "    model = FullyConnectedNet([100, 100, 100, 100, 100], weight_scale=5e-2)\n",
    "\n",
    "    solver = Solver(model, small_data,\n",
    "                  num_epochs=5, batch_size=100,\n",
    "                  update_rule=update_rule,\n",
    "                  optim_config={\n",
    "                    'learning_rate': learning_rates[update_rule]\n",
    "                  },\n",
    "                  verbose=True)\n",
    "    solvers[update_rule] = solver\n",
    "    solver.train()\n",
    "    print()\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "for update_rule, solver in list(solvers.items()):\n",
    "    plt.subplot(3, 1, 1)\n",
    "    plt.plot(solver.loss_history, 'o', label=update_rule)\n",
    "  \n",
    "    plt.subplot(3, 1, 2)\n",
    "    plt.plot(solver.train_acc_history, '-o', label=update_rule)\n",
    "\n",
    "    plt.subplot(3, 1, 3)\n",
    "    plt.plot(solver.val_acc_history, '-o', label=update_rule)\n",
    "    \n",
    "for i in [1, 2, 3]:\n",
    "    plt.subplot(3, 1, i)\n",
    "    plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inline Question 3:\n",
    "\n",
    "AdaGrad, like Adam, is a per-parameter optimization method that uses the following update rule:\n",
    "\n",
    "```\n",
    "cache += dw**2\n",
    "w += - learning_rate * dw / (np.sqrt(cache) + eps)\n",
    "```\n",
    "\n",
    "John notices that when he was training a network with AdaGrad that the updates became very small, and that his network was learning slowly. Using your knowledge of the AdaGrad update rule, why do you think the updates would become very small? Would Adam have the same issue?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Answer: \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train a good model!\n",
    "Train the best fully-connected model that you can on CIFAR-10, storing your best model in the `best_model` variable. We require you to get at least 50% accuracy on the validation set using a fully-connected net.\n",
    "\n",
    "If you are careful it should be possible to get accuracies above 55%, but we don't require it for this part and won't assign extra credit for doing so. Later in the assignment we will ask you to train the best convolutional network that you can on CIFAR-10, and we would prefer that you spend your effort working on convolutional nets rather than fully-connected nets.\n",
    "\n",
    "You might find it useful to complete the `BatchNormalization.ipynb` and `Dropout.ipynb` notebooks before completing this part, since those techniques can help you train powerful models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 2450) loss: 2.472088\n",
      "(Epoch 0 / 5) train acc: 0.092000; val_acc: 0.122000\n",
      "(Iteration 11 / 2450) loss: 2.013690\n",
      "(Iteration 21 / 2450) loss: 1.887949\n",
      "(Iteration 31 / 2450) loss: 1.814388\n",
      "(Iteration 41 / 2450) loss: 2.035162\n",
      "(Iteration 51 / 2450) loss: 1.744148\n",
      "(Iteration 61 / 2450) loss: 1.921866\n",
      "(Iteration 71 / 2450) loss: 1.921365\n",
      "(Iteration 81 / 2450) loss: 1.747350\n",
      "(Iteration 91 / 2450) loss: 1.862682\n",
      "(Iteration 101 / 2450) loss: 1.756992\n",
      "(Iteration 111 / 2450) loss: 1.865354\n",
      "(Iteration 121 / 2450) loss: 1.634905\n",
      "(Iteration 131 / 2450) loss: 1.705383\n",
      "(Iteration 141 / 2450) loss: 1.726844\n",
      "(Iteration 151 / 2450) loss: 1.600335\n",
      "(Iteration 161 / 2450) loss: 1.528756\n",
      "(Iteration 171 / 2450) loss: 1.649094\n",
      "(Iteration 181 / 2450) loss: 1.603154\n",
      "(Iteration 191 / 2450) loss: 1.709904\n",
      "(Iteration 201 / 2450) loss: 1.730553\n",
      "(Iteration 211 / 2450) loss: 1.557217\n",
      "(Iteration 221 / 2450) loss: 1.599654\n",
      "(Iteration 231 / 2450) loss: 1.547621\n",
      "(Iteration 241 / 2450) loss: 1.612380\n",
      "(Iteration 251 / 2450) loss: 1.426110\n",
      "(Iteration 261 / 2450) loss: 1.429197\n",
      "(Iteration 271 / 2450) loss: 1.640696\n",
      "(Iteration 281 / 2450) loss: 1.689970\n",
      "(Iteration 291 / 2450) loss: 1.550542\n",
      "(Iteration 301 / 2450) loss: 1.657541\n",
      "(Iteration 311 / 2450) loss: 1.738571\n",
      "(Iteration 321 / 2450) loss: 1.517389\n",
      "(Iteration 331 / 2450) loss: 1.623403\n",
      "(Iteration 341 / 2450) loss: 1.595629\n",
      "(Iteration 351 / 2450) loss: 1.488460\n",
      "(Iteration 361 / 2450) loss: 1.568368\n",
      "(Iteration 371 / 2450) loss: 1.415134\n",
      "(Iteration 381 / 2450) loss: 1.580309\n",
      "(Iteration 391 / 2450) loss: 1.704617\n",
      "(Iteration 401 / 2450) loss: 1.433287\n",
      "(Iteration 411 / 2450) loss: 1.428431\n",
      "(Iteration 421 / 2450) loss: 1.589270\n",
      "(Iteration 431 / 2450) loss: 1.516228\n",
      "(Iteration 441 / 2450) loss: 1.517994\n",
      "(Iteration 451 / 2450) loss: 1.425384\n",
      "(Iteration 461 / 2450) loss: 1.479264\n",
      "(Iteration 471 / 2450) loss: 1.613239\n",
      "(Iteration 481 / 2450) loss: 1.319796\n",
      "(Epoch 1 / 5) train acc: 0.472000; val_acc: 0.476000\n",
      "(Iteration 491 / 2450) loss: 1.450622\n",
      "(Iteration 501 / 2450) loss: 1.529815\n",
      "(Iteration 511 / 2450) loss: 1.419249\n",
      "(Iteration 521 / 2450) loss: 1.380009\n",
      "(Iteration 531 / 2450) loss: 1.578027\n",
      "(Iteration 541 / 2450) loss: 1.643482\n",
      "(Iteration 551 / 2450) loss: 1.537670\n",
      "(Iteration 561 / 2450) loss: 1.464801\n",
      "(Iteration 571 / 2450) loss: 1.369704\n",
      "(Iteration 581 / 2450) loss: 1.522996\n",
      "(Iteration 591 / 2450) loss: 1.681653\n",
      "(Iteration 601 / 2450) loss: 1.467419\n",
      "(Iteration 611 / 2450) loss: 1.441804\n",
      "(Iteration 621 / 2450) loss: 1.260039\n",
      "(Iteration 631 / 2450) loss: 1.442297\n",
      "(Iteration 641 / 2450) loss: 1.288894\n",
      "(Iteration 651 / 2450) loss: 1.430047\n",
      "(Iteration 661 / 2450) loss: 1.454974\n",
      "(Iteration 671 / 2450) loss: 1.449286\n",
      "(Iteration 681 / 2450) loss: 1.411379\n",
      "(Iteration 691 / 2450) loss: 1.580768\n",
      "(Iteration 701 / 2450) loss: 1.391310\n",
      "(Iteration 711 / 2450) loss: 1.319080\n",
      "(Iteration 721 / 2450) loss: 1.655189\n",
      "(Iteration 731 / 2450) loss: 1.460468\n",
      "(Iteration 741 / 2450) loss: 1.432433\n",
      "(Iteration 751 / 2450) loss: 1.347544\n",
      "(Iteration 761 / 2450) loss: 1.484676\n",
      "(Iteration 771 / 2450) loss: 1.429645\n",
      "(Iteration 781 / 2450) loss: 1.219640\n",
      "(Iteration 791 / 2450) loss: 1.468606\n",
      "(Iteration 801 / 2450) loss: 1.477370\n",
      "(Iteration 811 / 2450) loss: 1.340495\n",
      "(Iteration 821 / 2450) loss: 1.281381\n",
      "(Iteration 831 / 2450) loss: 1.415321\n",
      "(Iteration 841 / 2450) loss: 1.454047\n",
      "(Iteration 851 / 2450) loss: 1.243717\n",
      "(Iteration 861 / 2450) loss: 1.341653\n",
      "(Iteration 871 / 2450) loss: 1.371165\n",
      "(Iteration 881 / 2450) loss: 1.402940\n",
      "(Iteration 891 / 2450) loss: 1.138487\n",
      "(Iteration 901 / 2450) loss: 1.632077\n",
      "(Iteration 911 / 2450) loss: 1.306237\n",
      "(Iteration 921 / 2450) loss: 1.265970\n",
      "(Iteration 931 / 2450) loss: 1.302773\n",
      "(Iteration 941 / 2450) loss: 1.412693\n",
      "(Iteration 951 / 2450) loss: 1.262069\n",
      "(Iteration 961 / 2450) loss: 1.361487\n",
      "(Iteration 971 / 2450) loss: 1.317163\n",
      "(Epoch 2 / 5) train acc: 0.533000; val_acc: 0.514000\n",
      "(Iteration 981 / 2450) loss: 1.544693\n",
      "(Iteration 991 / 2450) loss: 1.209083\n",
      "(Iteration 1001 / 2450) loss: 1.396998\n",
      "(Iteration 1011 / 2450) loss: 1.257502\n",
      "(Iteration 1021 / 2450) loss: 1.266334\n",
      "(Iteration 1031 / 2450) loss: 1.286284\n",
      "(Iteration 1041 / 2450) loss: 1.424964\n",
      "(Iteration 1051 / 2450) loss: 1.262595\n",
      "(Iteration 1061 / 2450) loss: 1.306076\n",
      "(Iteration 1071 / 2450) loss: 1.025366\n",
      "(Iteration 1081 / 2450) loss: 1.343143\n",
      "(Iteration 1091 / 2450) loss: 1.373491\n",
      "(Iteration 1101 / 2450) loss: 1.335971\n",
      "(Iteration 1111 / 2450) loss: 1.332265\n",
      "(Iteration 1121 / 2450) loss: 1.379798\n",
      "(Iteration 1131 / 2450) loss: 1.214480\n",
      "(Iteration 1141 / 2450) loss: 1.107104\n",
      "(Iteration 1151 / 2450) loss: 1.220606\n",
      "(Iteration 1161 / 2450) loss: 1.218520\n",
      "(Iteration 1171 / 2450) loss: 1.241586\n",
      "(Iteration 1181 / 2450) loss: 1.253715\n",
      "(Iteration 1191 / 2450) loss: 1.288810\n",
      "(Iteration 1201 / 2450) loss: 1.449328\n",
      "(Iteration 1211 / 2450) loss: 1.189961\n",
      "(Iteration 1221 / 2450) loss: 1.232137\n",
      "(Iteration 1231 / 2450) loss: 1.300991\n",
      "(Iteration 1241 / 2450) loss: 1.335933\n",
      "(Iteration 1251 / 2450) loss: 1.280954\n",
      "(Iteration 1261 / 2450) loss: 1.248386\n",
      "(Iteration 1271 / 2450) loss: 1.220318\n",
      "(Iteration 1281 / 2450) loss: 1.515093\n",
      "(Iteration 1291 / 2450) loss: 1.331628\n",
      "(Iteration 1301 / 2450) loss: 1.439916\n",
      "(Iteration 1311 / 2450) loss: 1.210348\n",
      "(Iteration 1321 / 2450) loss: 1.137067\n",
      "(Iteration 1331 / 2450) loss: 1.352151\n",
      "(Iteration 1341 / 2450) loss: 1.135225\n",
      "(Iteration 1351 / 2450) loss: 1.225864\n",
      "(Iteration 1361 / 2450) loss: 1.153919\n",
      "(Iteration 1371 / 2450) loss: 1.078376\n",
      "(Iteration 1381 / 2450) loss: 1.370903\n",
      "(Iteration 1391 / 2450) loss: 1.410747\n",
      "(Iteration 1401 / 2450) loss: 1.213760\n",
      "(Iteration 1411 / 2450) loss: 1.181425\n",
      "(Iteration 1421 / 2450) loss: 1.288669\n",
      "(Iteration 1431 / 2450) loss: 1.226981\n",
      "(Iteration 1441 / 2450) loss: 1.258864\n",
      "(Iteration 1451 / 2450) loss: 1.241277\n",
      "(Iteration 1461 / 2450) loss: 1.322088\n",
      "(Epoch 3 / 5) train acc: 0.593000; val_acc: 0.525000\n",
      "(Iteration 1471 / 2450) loss: 1.149963\n",
      "(Iteration 1481 / 2450) loss: 1.241925\n",
      "(Iteration 1491 / 2450) loss: 1.146314\n",
      "(Iteration 1501 / 2450) loss: 1.122677\n",
      "(Iteration 1511 / 2450) loss: 1.336323\n",
      "(Iteration 1521 / 2450) loss: 1.164209\n",
      "(Iteration 1531 / 2450) loss: 1.147018\n",
      "(Iteration 1541 / 2450) loss: 1.244480\n",
      "(Iteration 1551 / 2450) loss: 1.163731\n",
      "(Iteration 1561 / 2450) loss: 1.131332\n",
      "(Iteration 1571 / 2450) loss: 1.279770\n",
      "(Iteration 1581 / 2450) loss: 1.276571\n",
      "(Iteration 1591 / 2450) loss: 1.095339\n",
      "(Iteration 1601 / 2450) loss: 1.173709\n",
      "(Iteration 1611 / 2450) loss: 1.224159\n",
      "(Iteration 1621 / 2450) loss: 1.113730\n",
      "(Iteration 1631 / 2450) loss: 1.133409\n",
      "(Iteration 1641 / 2450) loss: 1.063971\n",
      "(Iteration 1651 / 2450) loss: 1.126830\n",
      "(Iteration 1661 / 2450) loss: 1.249040\n",
      "(Iteration 1671 / 2450) loss: 1.196021\n",
      "(Iteration 1681 / 2450) loss: 1.272734\n",
      "(Iteration 1691 / 2450) loss: 1.023605\n",
      "(Iteration 1701 / 2450) loss: 1.154309\n",
      "(Iteration 1711 / 2450) loss: 1.164311\n",
      "(Iteration 1721 / 2450) loss: 1.138949\n",
      "(Iteration 1731 / 2450) loss: 1.251310\n",
      "(Iteration 1741 / 2450) loss: 1.185685\n",
      "(Iteration 1751 / 2450) loss: 1.008249\n",
      "(Iteration 1761 / 2450) loss: 1.117296\n",
      "(Iteration 1771 / 2450) loss: 1.204671\n",
      "(Iteration 1781 / 2450) loss: 1.185464\n",
      "(Iteration 1791 / 2450) loss: 1.137477\n",
      "(Iteration 1801 / 2450) loss: 1.329107\n",
      "(Iteration 1811 / 2450) loss: 1.180341\n",
      "(Iteration 1821 / 2450) loss: 1.180445\n",
      "(Iteration 1831 / 2450) loss: 1.209625\n",
      "(Iteration 1841 / 2450) loss: 1.137775\n",
      "(Iteration 1851 / 2450) loss: 1.120222\n",
      "(Iteration 1861 / 2450) loss: 0.961446\n",
      "(Iteration 1871 / 2450) loss: 1.197837\n",
      "(Iteration 1881 / 2450) loss: 1.142907\n",
      "(Iteration 1891 / 2450) loss: 1.054735\n",
      "(Iteration 1901 / 2450) loss: 1.264209\n",
      "(Iteration 1911 / 2450) loss: 1.131403\n",
      "(Iteration 1921 / 2450) loss: 1.208643\n",
      "(Iteration 1931 / 2450) loss: 1.035658\n",
      "(Iteration 1941 / 2450) loss: 1.179493\n",
      "(Iteration 1951 / 2450) loss: 1.212477\n",
      "(Epoch 4 / 5) train acc: 0.609000; val_acc: 0.535000\n",
      "(Iteration 1961 / 2450) loss: 1.071324\n",
      "(Iteration 1971 / 2450) loss: 0.972763\n",
      "(Iteration 1981 / 2450) loss: 1.024567\n",
      "(Iteration 1991 / 2450) loss: 1.137919\n",
      "(Iteration 2001 / 2450) loss: 0.992616\n",
      "(Iteration 2011 / 2450) loss: 1.053379\n",
      "(Iteration 2021 / 2450) loss: 1.106702\n",
      "(Iteration 2031 / 2450) loss: 1.029864\n",
      "(Iteration 2041 / 2450) loss: 1.031081\n",
      "(Iteration 2051 / 2450) loss: 0.927952\n",
      "(Iteration 2061 / 2450) loss: 1.140791\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 2071 / 2450) loss: 1.259309\n",
      "(Iteration 2081 / 2450) loss: 1.067790\n",
      "(Iteration 2091 / 2450) loss: 1.161826\n",
      "(Iteration 2101 / 2450) loss: 1.299634\n",
      "(Iteration 2111 / 2450) loss: 1.352126\n",
      "(Iteration 2121 / 2450) loss: 0.986742\n",
      "(Iteration 2131 / 2450) loss: 1.111428\n",
      "(Iteration 2141 / 2450) loss: 1.231669\n",
      "(Iteration 2151 / 2450) loss: 0.911781\n",
      "(Iteration 2161 / 2450) loss: 1.226662\n",
      "(Iteration 2171 / 2450) loss: 1.222389\n",
      "(Iteration 2181 / 2450) loss: 0.985872\n",
      "(Iteration 2191 / 2450) loss: 1.135291\n",
      "(Iteration 2201 / 2450) loss: 1.139999\n",
      "(Iteration 2211 / 2450) loss: 1.320171\n",
      "(Iteration 2221 / 2450) loss: 1.105819\n",
      "(Iteration 2231 / 2450) loss: 1.130510\n",
      "(Iteration 2241 / 2450) loss: 1.214042\n",
      "(Iteration 2251 / 2450) loss: 1.221498\n",
      "(Iteration 2261 / 2450) loss: 1.152588\n",
      "(Iteration 2271 / 2450) loss: 1.153435\n",
      "(Iteration 2281 / 2450) loss: 1.085887\n",
      "(Iteration 2291 / 2450) loss: 1.058718\n",
      "(Iteration 2301 / 2450) loss: 1.035678\n",
      "(Iteration 2311 / 2450) loss: 1.106288\n",
      "(Iteration 2321 / 2450) loss: 1.039511\n",
      "(Iteration 2331 / 2450) loss: 1.148556\n",
      "(Iteration 2341 / 2450) loss: 1.270665\n",
      "(Iteration 2351 / 2450) loss: 1.131036\n",
      "(Iteration 2361 / 2450) loss: 1.043852\n",
      "(Iteration 2371 / 2450) loss: 1.161735\n",
      "(Iteration 2381 / 2450) loss: 1.067380\n",
      "(Iteration 2391 / 2450) loss: 1.196029\n",
      "(Iteration 2401 / 2450) loss: 0.894275\n",
      "(Iteration 2411 / 2450) loss: 0.945790\n",
      "(Iteration 2421 / 2450) loss: 0.980619\n",
      "(Iteration 2431 / 2450) loss: 1.064556\n",
      "(Iteration 2441 / 2450) loss: 0.957004\n",
      "(Epoch 5 / 5) train acc: 0.673000; val_acc: 0.554000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\Anaconda_install\\envs\\cs231n\\lib\\site-packages\\matplotlib\\cbook\\deprecation.py:107: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  warnings.warn(message, mplDeprecation, stacklevel=1)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_model = None\n",
    "################################################################################\n",
    "# TODO: Train the best FullyConnectedNet that you can on CIFAR-10. You might   #\n",
    "# find batch/layer normalization and dropout useful. Store your best model in  #\n",
    "# the best_model variable.                                                     #\n",
    "################################################################################\n",
    "best_val_acc=0\n",
    "solver_s={}\n",
    "learning_rates=[5e-3]\n",
    "reg_list=[0]\n",
    "for j in range(1):\n",
    "    for i in range(1):\n",
    "        model_l = FullyConnectedNet([500, 500, 500, 500, 500, 500], weight_scale=5e-2\n",
    "                         ,dropout=1, normalization='batchnorm',reg=reg_list[j])\n",
    "        \n",
    "        solver_r = Solver(model_l, data,\n",
    "                  num_epochs=5, batch_size=100,\n",
    "                  update_rule='adam',print_every=10,lr_decay=0.95,\n",
    "                  optim_config={\n",
    "                    'learning_rate': learning_rates[i]\n",
    "                  },\n",
    "                  verbose=True)\n",
    "        solver_s['Lr:'+str(learning_rates[i])+', Reg:'+str(reg_list[j])]=solver_r\n",
    "        solver_r.train()\n",
    "        y_val_predict = np.argmax(model_l.loss(data['X_val']), axis=1)\n",
    "        val_accc=(y_val_predict == data['y_val']).mean()\n",
    "        if val_accc>best_val_acc:\n",
    "            best_model=model_l\n",
    "            \n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "for k, solver in list(solver_s.items()):\n",
    "    plt.subplot(3, 1, 1)\n",
    "    plt.plot(solver.loss_history, '.', label=k)\n",
    "  \n",
    "    plt.subplot(3, 1, 2)\n",
    "    plt.plot(solver.train_acc_history, '-o', label=k)\n",
    "\n",
    "    plt.subplot(3, 1, 3)\n",
    "    plt.plot(solver.val_acc_history, '-o', label=k)\n",
    "    \n",
    "for i in [1, 2, 3]:\n",
    "    plt.subplot(3, 1, i)\n",
    "    plt.legend(bbox_to_anchor=(1.03,0.97),loc=3)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()\n",
    "################################################################################\n",
    "#                              END OF YOUR CODE                                #\n",
    "################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test your model!\n",
    "Run your best model on the validation and test sets. You should achieve above 50% accuracy on the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation set accuracy:  0.554\n",
      "Test set accuracy:  0.547\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = np.argmax(best_model.loss(data['X_test']), axis=1)\n",
    "y_val_pred = np.argmax(best_model.loss(data['X_val']), axis=1)\n",
    "print('Validation set accuracy: ', (y_val_pred == data['y_val']).mean())\n",
    "print('Test set accuracy: ', (y_test_pred == data['y_test']).mean())"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
